Hierarchical Feature Selection with Recursive Regularization
Authors: Hong Zhao, Pengfei Zhu, Ping Wang, Qinghua Hu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public datasets demonstrate the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Academia | 1Tianjin University, China 2Lab of Granular Computing, Minnan Normal University, China |
| Pseudocode | Yes | Algorithm 1 Hierarchical Feature Selection with Recursive Regularization (Hi FSRR) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release, or mention of code in supplementary materials) for the source code. |
| Open Datasets | Yes | Two protein tasks include: F194 [Wei et al., 2015] and DD [Ding and Dubchak, 2001]. Four image tasks include: CLEF [Dimitrovski et al., 2011], CIFAR-100 [Krizhevsky and Hinton, 2009], PASCAL Visual Object Classes (VOC) [Everingham et al., 2010], and Scene UNderstanding (SUN) [Xiao et al., 2010]. |
| Dataset Splits | Yes | We select features on training sets and test them on test sets using 10fold cross validation. |
| Hardware Specification | Yes | All experiments are executed on an Intel Core i7-3770 running at 3.40 GHz with 12.0 GB memory and 64-bit Windows 7 operating system. |
| Software Dependencies | No | The paper mentions using SVM for classification, but does not provide specific software dependencies with version numbers (e.g., the specific SVM library and its version). |
| Experiment Setup | Yes | In the experiments, we set λ = 1, β = 1, and α = 1 for the CLEF dataset, and set λ = 10, β = 0.1, and α = 0.1 for the other datasets. |